A választhatóság lehetősége a tanulás információgyűjtő fázisában és a tanulásszervezésben ‒ alternatív iskolai gyakorlatok bemutatása az IPOO-modell alapján

A tanulmányban bemutatásra kerül néhány alternatív iskolai gyakorlat, melynek fókusza a tanulók választásának biztosítása a tanulás információszerző, ezen belül is a témaválasztás és forráskutatás, valamint a tanulásszervezés fázisában. A tanulás fázisai az IPOO-modell szerint kerülnek értelmezésre. A gyakorlatok bemutatása után néhány megoldást javaslunk, melyekkel hasonló választási lehetőségeket biztosíthatunk a tanulók számára hagyományos iskolai keretek között is. In this article some alternative school practices are presented, focusing on the choices of students during the information gathering phase of learning, including the topic selection and resource research as well as the learning organization phase. Learning phases are interpreted according to the IPOO-model. After the introduction of the practices we propose some solutions that will provide students with similar choices in the traditional school environment

Porosity Development Controlled by Deep-Burial Diagenetic Process in Lacustrine Sandstones Deposited in a Back-Arc Basin (Makó Trough, Pannonian Basin, Hungary)

Deeply buried Pannonian (Upper Miocene) siliciclastic deposits show evidence of secondary porosity development via dissolution processes at a late stage of diagenesis. This is demonstrated by detailed petrographic (optical, cathodoluminescence, fluorescence, and scanning electron microscopy) as well as elemental and stable isotope geochemical investigations of lacustrine deposits from the Makó Trough, the deepest depression within the extensional Pannonian back-arc basin. The analyses were carried out on core samples from six wells located in various positions from centre to margins of the trough. The paragenetic sequence of three formations was reconstructed with special emphasis on sandstone beds in a depth interval between ca. 2700 and 5500 m. The three formations consist, from bottom to top, of (1) open-water marls of the Endrőd Formation, which is a hydrocarbon source rock with locally derived coarse clastics and (2) a confined and (3) an unconfined turbidite system (respectively, the Szolnok and the Algyő Formation). In the sandstones, detrital grains consist of quartz, feldspar, and mica, as well as sedimentary and metamorphic rock fragments. The quartz content is high in the upper, unconfined turbidite formation (Algyő), whereas feldspars and rock fragments are more widespread in the lower formations (Szolnok and Endrőd). Eogenetic minerals are framboidal pyrite, calcite, and clay minerals. Mesogenetic minerals are ankerite, ferroan calcite, albite, quartz, illite, chlorite, and solid bituminous organic matter. Eogenetic finely crystalline calcite yielded δ13CV−PDB values from 1.4 to 0.7‰ and δ18OV−PDB values from –6.0 to –7.4‰, respectively. Mesogenetic ferroan calcite yielded δ13CV−PDB values from 2.6 to –1.2‰ and δ18OV−PDB values from –8.3 to –14.0‰, respectively. In the upper part of the turbidite systems, remnants of the migrated organic matter are preserved along pressure dissolution surfaces. All these features indicate that compaction and mineral precipitations resulted in tightly cemented sandstones prior to hydrocarbon migration. Interconnected, secondary, open porosity is associated with pyrite, kaolinite/dickite, and postdates of the late-stage calcite cement. This indicates that dissolution processes took place in the deep burial realm in an extraformational fluid-dominated diagenetic system. The findings of this study add a unique insight to the previously proposed hydrological model of the Pannonian Basin and describe the complex interactions between the basinal deposits and the basement blocks

A deep learning-based approach for high-throughput hypocotyl phenotyping

Hypocotyl length determination is a widely used method to phenotype young seedlings. The measurement itself has advanced from using rulers and millimetre papers to assessing digitized images but remains a labour-intensive, monotonous and time-consuming procedure. To make high-throughput plant phenotyping possible, we developed a deep learning-based approach to simplify and accelerate this method. Our pipeline does not require a specialized imaging system but works well with low-quality images produced with a simple flatbed scanner or a smartphone camera. Moreover, it is easily adaptable for a diverse range of datasets not restricted to Arabidopsis (Arabidopsis thaliana). Furthermore, we show that the accuracy of the method reaches human performance. We not only provide the full code at https://github.com/biomag-lab/hypocotyl-UNet, but also give detailed instructions on how the algorithm can be trained with custom data, tailoring it for the requirements and imaging setup of the user